The new Fujitsu AI Computing Broker Maximizing GPU utilization & minimize the environmental impact of AI

While the instinctive response to rising AI demand has been to purchase more GPUs, that arms race is proving unsustainable. Supply remains constrained while energy costs escalate, and most enterprises face pressure to achieve ambitious sustainability targets.

The future will belong not to the companies that own the most GPUs, but to those that use them most intelligently. In practice, this means shifting the focus from capacity to utilization. Every percentage point of additional GPU efficiency translates directly into cost savings, faster model development, and more room to experiment with next-generation workloads.

This is especially crucial as AI becomes mission-critical rather than experimental. Drug discovery, supply chain optimization, fraud detection, and customer service personalization all rely on complex pipelines, where the marginal value of each GPU-hour is rising. To remain competitive, enterprises must ensure that GPUs are not only available, but fully and efficiently engaged.

Efficiency gains of this magnitude inevitably raise the question of trade-offs. Would dynamic orchestration slow down workloads or undermine performance?

In practice, the opposite has proven true. Consider the case of AlphaFold2, the breakthrough system for protein structure prediction. Its pipeline alternates between CPU-heavy searches and GPU-intensive modeling. In a conventional setup, each job occupies an entire GPU throughout, leading to significant idle time. With ACB, multiple jobs can share a GPU dynamically, filling in the gaps with other tasks. The result: Alphafold2 shows a 270% improvement per GPU. This means that while only 12 proteins could be processed per hour on a single A100, incorporating ACB enabled the processing of 32 proteins per hour.

Similarly, enterprises hosting multiple LLMs face uneven demand. Traditionally, each model is pinned to its own GPU, ensuring responsiveness but wasting capacity when some models sit idle. ACB allows multiple LLMs to dynamically share infrastructure, reclaiming and reallocating memory in real time when demand subsides. Early deployments show that organizations can host significantly more models on the same hardware footprint, reducing costs while maintaining performance.

These examples illustrate a broader truth: While ACB introduces minor overhead from memory transfers, the cluster-level efficiency gains far outweigh the cost. At the scale where enterprises operate, the key metric is not the speed of a single job but the overall throughput of the system. On this measure, ACB consistently delivers transformative improvements.

For business and technology leaders, the implications are profound.

First, ACB changes the economics of scale. By allowing enterprises to run more workloads on fewer GPUs, it reduces reliance on capital-intensive hardware purchases. In a market where GPU supply is scarce and prices volatile, this creates both a financial buffer and strategic resilience.

Second, the technology accelerates innovation cycles. Faster throughput translates into faster iteration, which in turn fuels more rapid discovery and deployment. Whether testing new drug candidates, refining risk models, or developing new customer experiences, speed compounds advantage.

Third, ACB supports sustainability goals. If AI infrastructure is destined to be a major consumer of electricity, then efficiency is not optional—it is an imperative. By consolidating workloads, enterprises can shrink their energy footprint and align AI growth with environmental commitments.

Seen through this lens, ACB is not just a tool for efficiency but a lever for competitiveness, enabling organizations to pursue ambitious AI strategies without being constrained by the limits of static infrastructure.

The benefits of ACB are not theoretical. Its impacts are already being demonstrated in early deployments across industries.

In life sciences, AlphaFold2 pipelines have achieved dramatic throughput improvements, enabling larger molecular libraries to be screened with fewer GPUs. In financial services, risk models are trained more efficiently, accelerating decision-making in volatile markets. In retail, AI-powered vision systems for stores have scaled more effectively, improving security and customer analytics without additional hardware. And in cloud services, providers have been able to expand GPU access for customers without expanding their physical footprints.

Across these domains, a consistent pattern emerges: ACB helps organizations unlock the hidden potential of the infrastructure they already own, extending the life of capital investments and enabling innovation that would otherwise be cost-prohibitive.

What makes Fujitsu’s AI Computing Broker so compelling is that it represents more than just an optimization. It signals a new paradigm for how enterprises think about compute resources.

In the old model, infrastructure was static and scarce, and success was defined by capacity ownership. In the emerging model, infrastructure is dynamic and fluid, with success defined by effective resource orchestration.

This transformation mirrors other moments in technology history, from the shift to virtualization in servers to the rise of cloud-native architectures. Each time, organizations that embraced dynamic resource management were able to outpace competitors who remained bound by static models. The same will be true in AI.